The present invention relates in general to novel configurations of trainable resistive crosspoint devices, which are referred to herein as resistive processing units (RPUs). More specifically, the present invention relates to artificial neural networks (ANNs) formed from crossbar arrays of two-terminal RPUs that provide local data storage and local data processing without the need for additional processing elements beyond the two-terminal RPU, thereby accelerating the ANN's ability to learn and implement algorithms such as online neural network training, matrix inversion, matrix decomposition and the like.
“Machine learning” is used to broadly describe a primary function of electronic systems that learn from data. In machine learning and cognitive science, ANNs are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs and are generally unknown. Crossbar arrays are high density, low cost circuit architectures used to form a variety of electronic circuits and devices, including ANN architectures, neuromorphic microchips and ultra-high density nonvolatile memory. A basic crossbar array configuration includes a set of conductive row wires and a set of conductive column wires formed to intersect the set of conductive row wires. The intersections between the two sets of wires are separated by so-called crosspoint devices, which can be formed from thin film material.